{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:OYLVOTIO37PWWGZEDMR2VPYIT4","short_pith_number":"pith:OYLVOTIO","schema_version":"1.0","canonical_sha256":"7617574d0edfdf6b1b241b23aabf089f1255e6c4e7ee842af225e73887f2175e","source":{"kind":"arxiv","id":"2109.12307","version":1},"attestation_state":"computed","paper":{"title":"Multi-Modal Multi-Instance Learning for Retinal Disease Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.CV","authors_text":"Dayong Ding, Hailan Lin, Jianchun Zhao, Jie Wang, Weihong Yu, Xirong Li, Yang Zhou, Youxin Chen","submitted_at":"2021-09-25T08:16:47Z","abstract_excerpt":"This paper attacks an emerging challenge of multi-modal retinal disease recognition. Given a multi-modal case consisting of a color fundus photo (CFP) and an array of OCT B-scan images acquired during an eye examination, we aim to build a deep neural network that recognizes multiple vision-threatening diseases for the given case. As the diagnostic efficacy of CFP and OCT is disease-dependent, the network's ability of being both selective and interpretable is important. Moreover, as both data acquisition and manual labeling are extremely expensive in the medical domain, the network has to be re"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2109.12307","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-25T08:16:47Z","cross_cats_sorted":["cs.AI","cs.MM"],"title_canon_sha256":"790a54572a267f20433caa0248ad02e789b3e27cf4fd9a9bf6cc01481c7f4f86","abstract_canon_sha256":"50a62a1045d7ed423ee890f6e49c52a835c1a44350eb93f572a8e86d49d49337"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:17:18.873362Z","signature_b64":"gEm2zlSCR1XgKTeUn2u6qhFna8MFEccFImUfWOaYuzZ9dJTveUIyCwDT3mXwYlx8fOIIG73Og/8rgs/0YqM3Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7617574d0edfdf6b1b241b23aabf089f1255e6c4e7ee842af225e73887f2175e","last_reissued_at":"2026-07-05T03:17:18.872833Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:17:18.872833Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Modal Multi-Instance Learning for Retinal Disease Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.CV","authors_text":"Dayong Ding, Hailan Lin, Jianchun Zhao, Jie Wang, Weihong Yu, Xirong Li, Yang Zhou, Youxin Chen","submitted_at":"2021-09-25T08:16:47Z","abstract_excerpt":"This paper attacks an emerging challenge of multi-modal retinal disease recognition. Given a multi-modal case consisting of a color fundus photo (CFP) and an array of OCT B-scan images acquired during an eye examination, we aim to build a deep neural network that recognizes multiple vision-threatening diseases for the given case. As the diagnostic efficacy of CFP and OCT is disease-dependent, the network's ability of being both selective and interpretable is important. Moreover, as both data acquisition and manual labeling are extremely expensive in the medical domain, the network has to be re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.12307","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.12307/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2109.12307","created_at":"2026-07-05T03:17:18.872909+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.12307v1","created_at":"2026-07-05T03:17:18.872909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.12307","created_at":"2026-07-05T03:17:18.872909+00:00"},{"alias_kind":"pith_short_12","alias_value":"OYLVOTIO37PW","created_at":"2026-07-05T03:17:18.872909+00:00"},{"alias_kind":"pith_short_16","alias_value":"OYLVOTIO37PWWGZE","created_at":"2026-07-05T03:17:18.872909+00:00"},{"alias_kind":"pith_short_8","alias_value":"OYLVOTIO","created_at":"2026-07-05T03:17:18.872909+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OYLVOTIO37PWWGZEDMR2VPYIT4","json":"https://pith.science/pith/OYLVOTIO37PWWGZEDMR2VPYIT4.json","graph_json":"https://pith.science/api/pith-number/OYLVOTIO37PWWGZEDMR2VPYIT4/graph.json","events_json":"https://pith.science/api/pith-number/OYLVOTIO37PWWGZEDMR2VPYIT4/events.json","paper":"https://pith.science/paper/OYLVOTIO"},"agent_actions":{"view_html":"https://pith.science/pith/OYLVOTIO37PWWGZEDMR2VPYIT4","download_json":"https://pith.science/pith/OYLVOTIO37PWWGZEDMR2VPYIT4.json","view_paper":"https://pith.science/paper/OYLVOTIO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.12307&json=true","fetch_graph":"https://pith.science/api/pith-number/OYLVOTIO37PWWGZEDMR2VPYIT4/graph.json","fetch_events":"https://pith.science/api/pith-number/OYLVOTIO37PWWGZEDMR2VPYIT4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OYLVOTIO37PWWGZEDMR2VPYIT4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OYLVOTIO37PWWGZEDMR2VPYIT4/action/storage_attestation","attest_author":"https://pith.science/pith/OYLVOTIO37PWWGZEDMR2VPYIT4/action/author_attestation","sign_citation":"https://pith.science/pith/OYLVOTIO37PWWGZEDMR2VPYIT4/action/citation_signature","submit_replication":"https://pith.science/pith/OYLVOTIO37PWWGZEDMR2VPYIT4/action/replication_record"}},"created_at":"2026-07-05T03:17:18.872909+00:00","updated_at":"2026-07-05T03:17:18.872909+00:00"}